Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations23072
Missing cells106048
Missing cells (%)32.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 MiB
Average record size in memory112.0 B

Variable types

Numeric12
Categorical1
Text1

Alerts

cantidad_hechos is highly overall correlated with cantidad_victimas and 6 other fieldsHigh correlation
cantidad_victimas is highly overall correlated with cantidad_hechos and 7 other fieldsHigh correlation
cantidad_victimas_fem is highly overall correlated with cantidad_hechos and 6 other fieldsHigh correlation
cantidad_victimas_masc is highly overall correlated with cantidad_hechos and 5 other fieldsHigh correlation
cantidad_victimas_sd is highly overall correlated with cantidad_hechos and 2 other fieldsHigh correlation
provincia_id is highly overall correlated with provincia_nombreHigh correlation
provincia_nombre is highly overall correlated with provincia_idHigh correlation
tasa_hechos is highly overall correlated with cantidad_hechos and 5 other fieldsHigh correlation
tasa_victimas is highly overall correlated with cantidad_hechos and 7 other fieldsHigh correlation
tasa_victimas_fem is highly overall correlated with cantidad_hechos and 6 other fieldsHigh correlation
tasa_victimas_masc is highly overall correlated with cantidad_victimas and 5 other fieldsHigh correlation
codigo_delito_snic_id has 5024 (21.8%) missing values Missing
cantidad_victimas has 14432 (62.6%) missing values Missing
cantidad_victimas_masc has 14432 (62.6%) missing values Missing
cantidad_victimas_fem has 14432 (62.6%) missing values Missing
cantidad_victimas_sd has 14432 (62.6%) missing values Missing
tasa_victimas has 14432 (62.6%) missing values Missing
tasa_victimas_masc has 14432 (62.6%) missing values Missing
tasa_victimas_fem has 14432 (62.6%) missing values Missing
cantidad_victimas_sd is highly skewed (γ1 = 25.11153212) Skewed
provincia_nombre is uniformly distributed Uniform
cantidad_hechos has 3427 (14.9%) zeros Zeros
cantidad_victimas has 1160 (5.0%) zeros Zeros
cantidad_victimas_masc has 2496 (10.8%) zeros Zeros
cantidad_victimas_fem has 2499 (10.8%) zeros Zeros
cantidad_victimas_sd has 3957 (17.2%) zeros Zeros
tasa_hechos has 3427 (14.9%) zeros Zeros
tasa_victimas has 1160 (5.0%) zeros Zeros
tasa_victimas_masc has 2496 (10.8%) zeros Zeros
tasa_victimas_fem has 2499 (10.8%) zeros Zeros

Reproduction

Analysis started2025-04-28 18:24:50.468634
Analysis finished2025-04-28 18:25:12.137345
Duration21.67 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

provincia_id
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.008322
Minimum2
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size180.4 KiB
2025-04-28T15:25:12.300362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q125
median48
Q371
95-th percentile90
Maximum94
Range92
Interquartile range (IQR)46

Descriptive statistics

Standard deviation27.685671
Coefficient of variation (CV)0.57668484
Kurtosis-1.2041503
Mean48.008322
Median Absolute Deviation (MAD)24
Skewness-0.00020056686
Sum1107648
Variance766.4964
MonotonicityIncreasing
2025-04-28T15:25:12.397242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
22 968
 
4.2%
86 968
 
4.2%
66 968
 
4.2%
42 968
 
4.2%
14 960
 
4.2%
2 960
 
4.2%
10 960
 
4.2%
6 960
 
4.2%
30 960
 
4.2%
26 960
 
4.2%
Other values (14) 13440
58.3%
ValueCountFrequency (%)
2 960
4.2%
6 960
4.2%
10 960
4.2%
14 960
4.2%
18 960
4.2%
22 968
4.2%
26 960
4.2%
30 960
4.2%
34 960
4.2%
38 960
4.2%
ValueCountFrequency (%)
94 960
4.2%
90 960
4.2%
86 968
4.2%
82 960
4.2%
78 960
4.2%
74 960
4.2%
70 960
4.2%
66 968
4.2%
62 960
4.2%
58 960
4.2%

provincia_nombre
Categorical

High correlation  Uniform 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size180.4 KiB
Chaco
 
968
Santiago del Estero
 
968
Salta
 
968
La Pampa
 
968
Córdoba
 
960
Other values (19)
18240 

Length

Max length53
Median length19
Mean length11.122399
Min length5

Characters and Unicode

Total characters256616
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCiudad Autónoma de Buenos Aires
2nd rowCiudad Autónoma de Buenos Aires
3rd rowCiudad Autónoma de Buenos Aires
4th rowCiudad Autónoma de Buenos Aires
5th rowCiudad Autónoma de Buenos Aires

Common Values

ValueCountFrequency (%)
Chaco 968
 
4.2%
Santiago del Estero 968
 
4.2%
Salta 968
 
4.2%
La Pampa 968
 
4.2%
Córdoba 960
 
4.2%
Ciudad Autónoma de Buenos Aires 960
 
4.2%
Catamarca 960
 
4.2%
Buenos Aires 960
 
4.2%
Entre Ríos 960
 
4.2%
Chubut 960
 
4.2%
Other values (14) 13440
58.3%

Length

2025-04-28T15:25:12.506489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
del 2888
 
6.4%
la 1928
 
4.3%
aires 1920
 
4.3%
buenos 1920
 
4.3%
san 1920
 
4.3%
santa 1920
 
4.3%
santiago 968
 
2.1%
chaco 968
 
2.1%
salta 968
 
2.1%
pampa 968
 
2.1%
Other values (30) 28808
63.8%

Most occurring characters

ValueCountFrequency (%)
a 27904
 
10.9%
22104
 
8.6%
e 19216
 
7.5%
o 17304
 
6.7%
n 16328
 
6.4%
u 16320
 
6.4%
r 14408
 
5.6%
t 13464
 
5.2%
s 12488
 
4.9%
i 11528
 
4.5%
Other values (31) 85552
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 256616
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 27904
 
10.9%
22104
 
8.6%
e 19216
 
7.5%
o 17304
 
6.7%
n 16328
 
6.4%
u 16320
 
6.4%
r 14408
 
5.6%
t 13464
 
5.2%
s 12488
 
4.9%
i 11528
 
4.5%
Other values (31) 85552
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 256616
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 27904
 
10.9%
22104
 
8.6%
e 19216
 
7.5%
o 17304
 
6.7%
n 16328
 
6.4%
u 16320
 
6.4%
r 14408
 
5.6%
t 13464
 
5.2%
s 12488
 
4.9%
i 11528
 
4.5%
Other values (31) 85552
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 256616
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 27904
 
10.9%
22104
 
8.6%
e 19216
 
7.5%
o 17304
 
6.7%
n 16328
 
6.4%
u 16320
 
6.4%
r 14408
 
5.6%
t 13464
 
5.2%
s 12488
 
4.9%
i 11528
 
4.5%
Other values (31) 85552
33.3%

anio
Real number (ℝ)

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.4494
Minimum2000
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size180.4 KiB
2025-04-28T15:25:12.594762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001
Q12007
median2015
Q32020
95-th percentile2023
Maximum2023
Range23
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.1475434
Coefficient of variation (CV)0.0035498997
Kurtosis-1.1745161
Mean2013.4494
Median Absolute Deviation (MAD)6
Skewness-0.37177029
Sum46454304
Variance51.087377
MonotonicityNot monotonic
2025-04-28T15:25:12.722913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2023 1728
 
7.5%
2022 1440
 
6.2%
2019 1440
 
6.2%
2021 1440
 
6.2%
2018 1440
 
6.2%
2020 1440
 
6.2%
2017 1440
 
6.2%
2016 800
 
3.5%
2007 744
 
3.2%
2006 744
 
3.2%
Other values (14) 10416
45.1%
ValueCountFrequency (%)
2000 744
3.2%
2001 744
3.2%
2002 744
3.2%
2003 744
3.2%
2004 744
3.2%
2005 744
3.2%
2006 744
3.2%
2007 744
3.2%
2008 744
3.2%
2009 744
3.2%
ValueCountFrequency (%)
2023 1728
7.5%
2022 1440
6.2%
2021 1440
6.2%
2020 1440
6.2%
2019 1440
6.2%
2018 1440
6.2%
2017 1440
6.2%
2016 800
3.5%
2015 744
3.2%
2014 744
3.2%

codigo_delito_snic_id
Real number (ℝ)

Missing 

Distinct32
Distinct (%)0.2%
Missing5024
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean16.170213
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size180.4 KiB
2025-04-28T15:25:12.856458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q324
95-th percentile30
Maximum32
Range31
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.0469831
Coefficient of variation (CV)0.55948448
Kurtosis-1.1990496
Mean16.170213
Median Absolute Deviation (MAD)8
Skewness0.0024004458
Sum291840
Variance81.847903
MonotonicityNot monotonic
2025-04-28T15:25:12.985722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 576
 
2.5%
2 576
 
2.5%
4 576
 
2.5%
3 576
 
2.5%
6 576
 
2.5%
7 576
 
2.5%
8 576
 
2.5%
5 576
 
2.5%
10 576
 
2.5%
11 576
 
2.5%
Other values (22) 12288
53.3%
(Missing) 5024
21.8%
ValueCountFrequency (%)
1 576
2.5%
2 576
2.5%
3 576
2.5%
4 576
2.5%
5 576
2.5%
6 576
2.5%
7 576
2.5%
8 576
2.5%
9 576
2.5%
10 576
2.5%
ValueCountFrequency (%)
32 192
 
0.8%
31 576
2.5%
30 576
2.5%
29 576
2.5%
28 576
2.5%
27 576
2.5%
26 576
2.5%
25 576
2.5%
24 576
2.5%
23 576
2.5%
Distinct69
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size180.4 KiB
2025-04-28T15:25:13.307779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length92
Median length47
Mean length35.858703
Min length6

Characters and Unicode

Total characters827332
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHomicidios dolosos
2nd rowHomicidios dolosos en grado de tentativa
3rd rowMuertes en accidentes viales
4th rowHomicidios culposos por otros hechos
5th rowLesiones dolosas
ValueCountFrequency (%)
de 9252
 
7.7%
delitos 8576
 
7.1%
contra 7488
 
6.2%
la 5160
 
4.3%
el 4800
 
4.0%
otros 4348
 
3.6%
lesiones 4032
 
3.3%
por 3456
 
2.9%
en 2644
 
2.2%
y 2548
 
2.1%
Other values (128) 68136
56.6%
2025-04-28T15:25:13.728263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
97368
11.8%
e 85784
10.4%
o 76888
 
9.3%
s 72684
 
8.8%
a 64056
 
7.7%
i 51468
 
6.2%
t 46168
 
5.6%
l 43380
 
5.2%
r 43212
 
5.2%
n 41120
 
5.0%
Other values (42) 205204
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 827332
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
97368
11.8%
e 85784
10.4%
o 76888
 
9.3%
s 72684
 
8.8%
a 64056
 
7.7%
i 51468
 
6.2%
t 46168
 
5.6%
l 43380
 
5.2%
r 43212
 
5.2%
n 41120
 
5.0%
Other values (42) 205204
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 827332
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
97368
11.8%
e 85784
10.4%
o 76888
 
9.3%
s 72684
 
8.8%
a 64056
 
7.7%
i 51468
 
6.2%
t 46168
 
5.6%
l 43380
 
5.2%
r 43212
 
5.2%
n 41120
 
5.0%
Other values (42) 205204
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 827332
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
97368
11.8%
e 85784
10.4%
o 76888
 
9.3%
s 72684
 
8.8%
a 64056
 
7.7%
i 51468
 
6.2%
t 46168
 
5.6%
l 43380
 
5.2%
r 43212
 
5.2%
n 41120
 
5.0%
Other values (42) 205204
24.8%

cantidad_hechos
Real number (ℝ)

High correlation  Zeros 

Distinct4514
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1673.5968
Minimum0
Maximum143840
Zeros3427
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size180.4 KiB
2025-04-28T15:25:13.850331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median77
Q3615
95-th percentile7902.75
Maximum143840
Range143840
Interquartile range (IQR)610

Descriptive statistics

Standard deviation6487.8279
Coefficient of variation (CV)3.8765775
Kurtosis95.027686
Mean1673.5968
Median Absolute Deviation (MAD)77
Skewness8.5061641
Sum38613226
Variance42091911
MonotonicityNot monotonic
2025-04-28T15:25:13.986650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3427
 
14.9%
1 920
 
4.0%
2 493
 
2.1%
3 410
 
1.8%
4 329
 
1.4%
5 262
 
1.1%
6 260
 
1.1%
8 209
 
0.9%
7 202
 
0.9%
10 165
 
0.7%
Other values (4504) 16395
71.1%
ValueCountFrequency (%)
0 3427
14.9%
1 920
 
4.0%
2 493
 
2.1%
3 410
 
1.8%
4 329
 
1.4%
5 262
 
1.1%
6 260
 
1.1%
7 202
 
0.9%
8 209
 
0.9%
9 154
 
0.7%
ValueCountFrequency (%)
143840 1
< 0.1%
132214 1
< 0.1%
114050 1
< 0.1%
107769 1
< 0.1%
106234 1
< 0.1%
104520 1
< 0.1%
101140 1
< 0.1%
98805 1
< 0.1%
97352 1
< 0.1%
95754 1
< 0.1%

cantidad_victimas
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1932
Distinct (%)22.4%
Missing14432
Missing (%)62.6%
Infinite0
Infinite (%)0.0%
Mean945.70683
Minimum0
Maximum68450
Zeros1160
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size180.4 KiB
2025-04-28T15:25:14.123908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median70
Q3360
95-th percentile3292.05
Maximum68450
Range68450
Interquartile range (IQR)350

Descriptive statistics

Standard deviation3756.6092
Coefficient of variation (CV)3.9722767
Kurtosis109.30584
Mean945.70683
Median Absolute Deviation (MAD)70
Skewness9.0951924
Sum8170907
Variance14112113
MonotonicityNot monotonic
2025-04-28T15:25:14.257070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1160
 
5.0%
1 195
 
0.8%
3 133
 
0.6%
2 127
 
0.6%
4 98
 
0.4%
5 94
 
0.4%
6 91
 
0.4%
7 77
 
0.3%
8 67
 
0.3%
10 63
 
0.3%
Other values (1922) 6535
28.3%
(Missing) 14432
62.6%
ValueCountFrequency (%)
0 1160
5.0%
1 195
 
0.8%
2 127
 
0.6%
3 133
 
0.6%
4 98
 
0.4%
5 94
 
0.4%
6 91
 
0.4%
7 77
 
0.3%
8 67
 
0.3%
9 56
 
0.2%
ValueCountFrequency (%)
68450 1
< 0.1%
68363 1
< 0.1%
64217 1
< 0.1%
62969 1
< 0.1%
61238 1
< 0.1%
60557 1
< 0.1%
59093 1
< 0.1%
58606 1
< 0.1%
47930 1
< 0.1%
44730 1
< 0.1%

cantidad_victimas_masc
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1296
Distinct (%)15.0%
Missing14432
Missing (%)62.6%
Infinite0
Infinite (%)0.0%
Mean395.26539
Minimum0
Maximum29567
Zeros2496
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size180.4 KiB
2025-04-28T15:25:14.515389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14
Q396.25
95-th percentile1363.05
Maximum29567
Range29567
Interquartile range (IQR)96.25

Descriptive statistics

Standard deviation1795.4152
Coefficient of variation (CV)4.542303
Kurtosis99.895033
Mean395.26539
Median Absolute Deviation (MAD)14
Skewness9.0187527
Sum3415093
Variance3223515.6
MonotonicityNot monotonic
2025-04-28T15:25:14.673600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2496
 
10.8%
1 282
 
1.2%
2 216
 
0.9%
3 196
 
0.8%
4 151
 
0.7%
7 128
 
0.6%
5 126
 
0.5%
6 124
 
0.5%
8 111
 
0.5%
9 104
 
0.5%
Other values (1286) 4706
 
20.4%
(Missing) 14432
62.6%
ValueCountFrequency (%)
0 2496
10.8%
1 282
 
1.2%
2 216
 
0.9%
3 196
 
0.8%
4 151
 
0.7%
5 126
 
0.5%
6 124
 
0.5%
7 128
 
0.6%
8 111
 
0.5%
9 104
 
0.5%
ValueCountFrequency (%)
29567 1
< 0.1%
29179 1
< 0.1%
29011 1
< 0.1%
26724 1
< 0.1%
26313 1
< 0.1%
25980 1
< 0.1%
25913 1
< 0.1%
25645 1
< 0.1%
24568 1
< 0.1%
24480 1
< 0.1%

cantidad_victimas_fem
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1253
Distinct (%)14.5%
Missing14432
Missing (%)62.6%
Infinite0
Infinite (%)0.0%
Mean343.46146
Minimum0
Maximum41317
Zeros2499
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size180.4 KiB
2025-04-28T15:25:14.821527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11
Q3112
95-th percentile1253.15
Maximum41317
Range41317
Interquartile range (IQR)112

Descriptive statistics

Standard deviation1613.6957
Coefficient of variation (CV)4.6983312
Kurtosis208.66821
Mean343.46146
Median Absolute Deviation (MAD)11
Skewness12.083046
Sum2967507
Variance2604013.8
MonotonicityNot monotonic
2025-04-28T15:25:14.939744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2499
 
10.8%
1 396
 
1.7%
2 263
 
1.1%
3 214
 
0.9%
4 197
 
0.9%
6 151
 
0.7%
5 144
 
0.6%
7 130
 
0.6%
8 117
 
0.5%
9 116
 
0.5%
Other values (1243) 4413
 
19.1%
(Missing) 14432
62.6%
ValueCountFrequency (%)
0 2499
10.8%
1 396
 
1.7%
2 263
 
1.1%
3 214
 
0.9%
4 197
 
0.9%
5 144
 
0.6%
6 151
 
0.7%
7 130
 
0.6%
8 117
 
0.5%
9 116
 
0.5%
ValueCountFrequency (%)
41317 1
< 0.1%
40884 1
< 0.1%
35910 1
< 0.1%
35109 1
< 0.1%
30489 1
< 0.1%
25137 1
< 0.1%
24652 1
< 0.1%
21381 1
< 0.1%
20878 1
< 0.1%
20765 1
< 0.1%

cantidad_victimas_sd
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct871
Distinct (%)10.1%
Missing14432
Missing (%)62.6%
Infinite0
Infinite (%)0.0%
Mean206.98125
Minimum0
Maximum61164
Zeros3957
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size180.4 KiB
2025-04-28T15:25:15.080984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q330
95-th percentile768.1
Maximum61164
Range61164
Interquartile range (IQR)30

Descriptive statistics

Standard deviation1582.4091
Coefficient of variation (CV)7.6451807
Kurtosis834.73454
Mean206.98125
Median Absolute Deviation (MAD)1
Skewness25.111532
Sum1788318
Variance2504018.4
MonotonicityNot monotonic
2025-04-28T15:25:15.219028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3957
 
17.2%
1 613
 
2.7%
2 329
 
1.4%
3 202
 
0.9%
4 170
 
0.7%
5 136
 
0.6%
6 103
 
0.4%
7 84
 
0.4%
8 76
 
0.3%
9 73
 
0.3%
Other values (861) 2897
 
12.6%
(Missing) 14432
62.6%
ValueCountFrequency (%)
0 3957
17.2%
1 613
 
2.7%
2 329
 
1.4%
3 202
 
0.9%
4 170
 
0.7%
5 136
 
0.6%
6 103
 
0.4%
7 84
 
0.4%
8 76
 
0.3%
9 73
 
0.3%
ValueCountFrequency (%)
61164 1
< 0.1%
59000 1
< 0.1%
58589 1
< 0.1%
44166 1
< 0.1%
43626 1
< 0.1%
14959 1
< 0.1%
14924 1
< 0.1%
14092 1
< 0.1%
13803 1
< 0.1%
13291 1
< 0.1%

tasa_hechos
Real number (ℝ)

High correlation  Zeros 

Distinct17953
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.20726
Minimum0
Maximum6707.5234
Zeros3427
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size180.4 KiB
2025-04-28T15:25:15.339081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.60205504
median8.8645592
Q357.685395
95-th percentile626.35281
Maximum6707.5234
Range6707.5234
Interquartile range (IQR)57.083339

Descriptive statistics

Standard deviation285.75921
Coefficient of variation (CV)2.5929255
Kurtosis53.276405
Mean110.20726
Median Absolute Deviation (MAD)8.8645592
Skewness5.5133437
Sum2542701.9
Variance81658.327
MonotonicityNot monotonic
2025-04-28T15:25:15.461110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3427
 
14.9%
0.16711788 12
 
0.1%
0.60626638 9
 
< 0.1%
0.13728663 9
 
< 0.1%
0.09166424 7
 
< 0.1%
0.26094672 7
 
< 0.1%
0.26162121 7
 
< 0.1%
0.26451737 7
 
< 0.1%
0.078431867 6
 
< 0.1%
0.23669983 6
 
< 0.1%
Other values (17943) 19575
84.8%
ValueCountFrequency (%)
0 3427
14.9%
0.005543388 3
 
< 0.1%
0.005594173 2
 
< 0.1%
0.0056466553 4
 
< 0.1%
0.0057008835 2
 
< 0.1%
0.0057570045 1
 
< 0.1%
0.0058151721 6
 
< 0.1%
0.0058754366 1
 
< 0.1%
0.0060024257 1
 
< 0.1%
0.0060693794 2
 
< 0.1%
ValueCountFrequency (%)
6707.5234 1
< 0.1%
5636.7183 1
< 0.1%
5150.3057 1
< 0.1%
4774.3257 1
< 0.1%
4712.481 1
< 0.1%
4412.6094 1
< 0.1%
4162.0215 1
< 0.1%
3958.4583 1
< 0.1%
3400.1006 1
< 0.1%
3294.46 1
< 0.1%

tasa_victimas
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct7259
Distinct (%)84.0%
Missing14432
Missing (%)62.6%
Infinite0
Infinite (%)0.0%
Mean58.851241
Minimum0
Maximum1232.8198
Zeros1160
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size180.4 KiB
2025-04-28T15:25:15.612442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.5846398
median8.4762006
Q329.360937
95-th percentile376.91968
Maximum1232.8198
Range1232.8198
Interquartile range (IQR)27.776297

Descriptive statistics

Standard deviation139.44691
Coefficient of variation (CV)2.3694812
Kurtosis14.414186
Mean58.851241
Median Absolute Deviation (MAD)8.2892957
Skewness3.5932354
Sum508474.72
Variance19445.44
MonotonicityNot monotonic
2025-04-28T15:25:15.749843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1160
 
5.0%
1.0164384 3
 
< 0.1%
0.77242744 3
 
< 0.1%
1.4185363 3
 
< 0.1%
0.24464233 3
 
< 0.1%
0.23283321 3
 
< 0.1%
1.8518666 3
 
< 0.1%
0.26162121 3
 
< 0.1%
0.87885821 3
 
< 0.1%
36.316162 3
 
< 0.1%
Other values (7249) 7453
32.3%
(Missing) 14432
62.6%
ValueCountFrequency (%)
0 1160
5.0%
0.005543388 1
 
< 0.1%
0.0058151721 1
 
< 0.1%
0.0060024257 1
 
< 0.1%
0.0062109418 1
 
< 0.1%
0.0062855105 1
 
< 0.1%
0.0064363782 1
 
< 0.1%
0.0067367144 1
 
< 0.1%
0.012421884 1
 
< 0.1%
0.012571021 2
 
< 0.1%
ValueCountFrequency (%)
1232.8198 1
< 0.1%
1195.661 1
< 0.1%
1195.6143 1
< 0.1%
1150.7468 1
< 0.1%
1147.5549 1
< 0.1%
1064.4044 1
< 0.1%
1040.3544 1
< 0.1%
1017.0685 1
< 0.1%
1001.4467 1
< 0.1%
998.42242 1
< 0.1%

tasa_victimas_masc
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct5757
Distinct (%)66.6%
Missing14432
Missing (%)62.6%
Infinite0
Infinite (%)0.0%
Mean49.172893
Minimum0
Maximum1476.9515
Zeros2496
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size180.4 KiB
2025-04-28T15:25:15.871458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.5822547
Q318.288994
95-th percentile335.38045
Maximum1476.9515
Range1476.9515
Interquartile range (IQR)18.288994

Descriptive statistics

Standard deviation134.42601
Coefficient of variation (CV)2.7337421
Kurtosis21.561088
Mean49.172893
Median Absolute Deviation (MAD)3.5822547
Skewness4.2091299
Sum424853.79
Variance18070.352
MonotonicityNot monotonic
2025-04-28T15:25:15.998435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2496
 
10.8%
2.1075926 4
 
< 0.1%
1.0077648 4
 
< 0.1%
0.013171958 4
 
< 0.1%
0.48521078 3
 
< 0.1%
1.8582259 3
 
< 0.1%
1.1233494 3
 
< 0.1%
1.3092496 3
 
< 0.1%
1.0343187 3
 
< 0.1%
0.36643326 3
 
< 0.1%
Other values (5747) 6114
26.5%
(Missing) 14432
62.6%
ValueCountFrequency (%)
0 2496
10.8%
0.011392054 1
 
< 0.1%
0.011502999 1
 
< 0.1%
0.011736555 2
 
< 0.1%
0.01185982 1
 
< 0.1%
0.012700535 2
 
< 0.1%
0.013171958 4
 
< 0.1%
0.014503482 1
 
< 0.1%
0.025401071 1
 
< 0.1%
0.025718428 1
 
< 0.1%
ValueCountFrequency (%)
1476.9515 1
< 0.1%
1414.6658 1
< 0.1%
1370.5457 1
< 0.1%
1254.9751 1
< 0.1%
1217.9423 1
< 0.1%
1191.9841 1
< 0.1%
1183.6864 1
< 0.1%
1168.6772 1
< 0.1%
1164.1257 1
< 0.1%
1105.7815 1
< 0.1%

tasa_victimas_fem
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct5771
Distinct (%)66.8%
Missing14432
Missing (%)62.6%
Infinite0
Infinite (%)0.0%
Mean45.112229
Minimum0
Maximum1462.505
Zeros2499
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size180.4 KiB
2025-04-28T15:25:16.138304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.3392582
Q322.387281
95-th percentile281.39877
Maximum1462.505
Range1462.505
Interquartile range (IQR)22.387281

Descriptive statistics

Standard deviation126.56394
Coefficient of variation (CV)2.805535
Kurtosis27.643004
Mean45.112229
Median Absolute Deviation (MAD)2.3392582
Skewness4.705653
Sum389769.66
Variance16018.431
MonotonicityNot monotonic
2025-04-28T15:25:16.276067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2499
 
10.8%
1.0005302 4
 
< 0.1%
4.6565051 4
 
< 0.1%
1.975777 4
 
< 0.1%
0.68230045 3
 
< 0.1%
1.5202945 3
 
< 0.1%
0.54449922 3
 
< 0.1%
0.33767357 3
 
< 0.1%
7.5037518 3
 
< 0.1%
1.0120842 3
 
< 0.1%
Other values (5761) 6111
26.5%
(Missing) 14432
62.6%
ValueCountFrequency (%)
0 2499
10.8%
0.011299746 1
 
< 0.1%
0.01140958 1
 
< 0.1%
0.011762665 1
 
< 0.1%
0.012155197 3
 
< 0.1%
0.012295462 3
 
< 0.1%
0.012440364 1
 
< 0.1%
0.012735059 1
 
< 0.1%
0.013882478 1
 
< 0.1%
0.022182262 1
 
< 0.1%
ValueCountFrequency (%)
1462.505 1
< 0.1%
1436.4557 1
< 0.1%
1417.0071 1
< 0.1%
1308.9484 1
< 0.1%
1308.7645 1
< 0.1%
1297.3264 1
< 0.1%
1248.1548 1
< 0.1%
1200.6664 1
< 0.1%
1124.038 1
< 0.1%
1119.8145 1
< 0.1%

Interactions

2025-04-28T15:25:09.906852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:51.720601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:53.213427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:54.730851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:57.904818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:59.685810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:01.405542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:02.862739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:04.175605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:05.543001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:07.262468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:08.572869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:10.003945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:51.865768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:53.309913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:55.021085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:58.024644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:59.800790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:01.509933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:02.964679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:04.268870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:05.647424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:07.354021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:08.669923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:10.103731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:51.972414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:53.406486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:55.280040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:58.171831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:59.914670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:01.620652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:03.072747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:04.367934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:05.785070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:07.456404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:08.769828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:10.251945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:52.273714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:53.822356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:55.704315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:58.493278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:00.067715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:01.791967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:03.219497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:04.512777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:06.233251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:07.599894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:08.911262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:10.380853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:52.397123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:53.927134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:56.104343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:58.618948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:00.180848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:01.936891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:03.324140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:04.648171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:06.352354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:07.702735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:09.016768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:10.502653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:52.521142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:54.025333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:56.322350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:58.741039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:00.334045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:02.054102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:03.430008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:04.782696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:06.474474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:07.808924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:09.143222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:10.612875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:52.624286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:54.124958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:56.512808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:58.935473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:00.467377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:02.162512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:03.538839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:04.899452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:06.592728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:07.914183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:09.271377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:10.712689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:52.721830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:54.218809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:56.675390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:59.053888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:00.586408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:02.258166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:03.654991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:05.004371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:06.692253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:08.038870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:09.373830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:10.822043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:52.822966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:54.317217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:57.066400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:59.159581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:00.692040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:02.368788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:03.769690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:05.111714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:06.817787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:08.154997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:09.483040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:10.930166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:52.923873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:54.417120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:57.392028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:59.270863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:00.820795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:02.480320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:03.869492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:05.220286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:06.943122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:08.261442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:09.588054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:11.035514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:53.017840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:54.513036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:57.553768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:59.391230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:00.966482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:02.589299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:03.968169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:05.329766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:07.047730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:08.364991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:09.691327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:11.140841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:53.117266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:54.609715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:57.731429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:24:59.519043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:01.241693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:02.737313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:04.072569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:05.433385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:07.155727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:08.467962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T15:25:09.800290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-28T15:25:16.375701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
aniocantidad_hechoscantidad_victimascantidad_victimas_femcantidad_victimas_masccantidad_victimas_sdcodigo_delito_snic_idprovincia_idprovincia_nombretasa_hechostasa_victimastasa_victimas_femtasa_victimas_masc
anio1.000-0.128-0.1440.022-0.011-0.1930.3750.0000.000-0.160-0.186-0.003-0.037
cantidad_hechos-0.1281.0000.7920.5790.5520.517-0.205-0.0940.1160.9560.7090.5170.489
cantidad_victimas-0.1440.7921.0000.7600.7370.648-0.123-0.1070.1390.7040.9220.7020.678
cantidad_victimas_fem0.0220.5790.7601.0000.8920.250-0.182-0.0890.1120.5310.7190.9650.848
cantidad_victimas_masc-0.0110.5520.7370.8921.0000.200-0.393-0.0880.1460.4850.6780.8410.961
cantidad_victimas_sd-0.1930.5170.6480.2500.2001.0000.133-0.0830.1630.4460.5850.1940.140
codigo_delito_snic_id0.375-0.205-0.123-0.182-0.3930.1331.0000.0000.000-0.214-0.086-0.173-0.403
provincia_id0.000-0.094-0.107-0.089-0.088-0.0830.0001.0001.0000.0020.016-0.008-0.005
provincia_nombre0.0000.1160.1390.1120.1460.1630.0001.0001.0000.0620.1000.0830.098
tasa_hechos-0.1600.9560.7040.5310.4850.446-0.2140.0020.0621.0000.7850.5640.520
tasa_victimas-0.1860.7090.9220.7190.6780.585-0.0860.0160.1000.7851.0000.7510.712
tasa_victimas_fem-0.0030.5170.7020.9650.8410.194-0.173-0.0080.0830.5640.7511.0000.863
tasa_victimas_masc-0.0370.4890.6780.8480.9610.140-0.403-0.0050.0980.5200.7120.8631.000

Missing values

2025-04-28T15:25:11.355196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-28T15:25:11.540115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-28T15:25:11.993921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

provincia_idprovincia_nombreaniocodigo_delito_snic_idcodigo_delito_snic_nombrecantidad_hechoscantidad_victimascantidad_victimas_masccantidad_victimas_femcantidad_victimas_sdtasa_hechostasa_victimastasa_victimas_masctasa_victimas_fem
02Ciudad Autónoma de Buenos Aires20001Homicidios dolosos149158.0121.037.00.04.9750015.2755058.8257012.278360
12Ciudad Autónoma de Buenos Aires20002Homicidios dolosos en grado de tentativa5458.040.018.00.01.8030211.9365782.9175871.108391
22Ciudad Autónoma de Buenos Aires20003Muertes en accidentes viales121126.097.029.00.04.0401024.2070487.0751481.785742
32Ciudad Autónoma de Buenos Aires20004Homicidios culposos por otros hechos3536.020.013.03.01.1686241.2020141.4587930.800505
42Ciudad Autónoma de Buenos Aires20005Lesiones dolosas1315413732.08105.05485.0142.0439.202480458.501460591.176030337.751500
52Ciudad Autónoma de Buenos Aires20006Lesiones culposas en Accidentes Viales78908486.05433.02998.055.0263.441350283.341370396.281250184.608750
62Ciudad Autónoma de Buenos Aires20007Lesiones culposas por otros hechos11641244.0725.0480.039.038.86511241.53625552.88126429.557106
72Ciudad Autónoma de Buenos Aires20008Otros delitos contra las personas17841881.0928.0856.097.059.56646062.80522267.68801952.710171
82Ciudad Autónoma de Buenos Aires20009Delitos contra el honor00.00.00.00.00.0000000.0000000.0000000.000000
92Ciudad Autónoma de Buenos Aires200010Abusos sexuales con acceso carnal (violaciones)197206.025.0173.08.06.5776866.8781901.82349210.652873
provincia_idprovincia_nombreaniocodigo_delito_snic_idcodigo_delito_snic_nombrecantidad_hechoscantidad_victimascantidad_victimas_masccantidad_victimas_femcantidad_victimas_sdtasa_hechostasa_victimastasa_victimas_masctasa_victimas_fem
2306294Tierra del Fuego, Antártida e Islas del Atlántico Sur202329_2Ley de fauna16NaNNaNNaNNaN8.588990NaNNaNNaN
2306394Tierra del Fuego, Antártida e Islas del Atlántico Sur202329_3Delitos migratorios182NaNNaNNaNNaN97.699760NaNNaNNaN
2306494Tierra del Fuego, Antártida e Islas del Atlántico Sur202329_4Obstrucción del código aduanero2NaNNaNNaNNaN1.073624NaNNaNNaN
2306594Tierra del Fuego, Antártida e Islas del Atlántico Sur202329_5Contrabando Simple17NaNNaNNaNNaN9.125802NaNNaNNaN
2306694Tierra del Fuego, Antártida e Islas del Atlántico Sur202329_6Contrabando Agravado0NaNNaNNaNNaN0.000000NaNNaNNaN
2306794Tierra del Fuego, Antártida e Islas del Atlántico Sur202329_7Contrabando de elementos nucleares agresivos químicos armas y municiones1NaNNaNNaNNaN0.536812NaNNaNNaN
2306894Tierra del Fuego, Antártida e Islas del Atlántico Sur202329_8Otros delitos previstos en leyes especiales3029NaNNaNNaNNaN1626.003200NaNNaNNaN
2306994Tierra del Fuego, Antártida e Islas del Atlántico Sur202330Contravenciones227NaNNaNNaNNaN121.856290NaNNaNNaN
2307094Tierra del Fuego, Antártida e Islas del Atlántico Sur202331Suicidios (consumados)2020.016.04.00.011.67038211.67038218.3452574.752965
2307194Tierra del Fuego, Antártida e Islas del Atlántico Sur202332Delitos contra el orden económico y financiero8NaNNaNNaNNaN4.294495NaNNaNNaN